{"id":"W2005526465","doi":"10.1109/iscas.2010.5537102","title":"System-level design of low complexity CVNS feed forward neural network","year":2010,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Computer science; Artificial neural network; Robustness (evolution); Redundancy (engineering); Sensitivity (control systems); Modular design; Multiplexer; Algorithm; Electronic engineering; Multiplexing; Artificial intelligence; Engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003416934,0.0003857187,0.0003519194,0.0002791274,0.0004270402,0.00100847,0.001330817,0.0006362631,0.002479926],"category_scores_gemma":[0.0006416998,0.0002512797,0.000300433,0.0002357792,0.0003337045,0.0005895026,0.0003580869,0.0005795993,0.0004975601],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001167267,"about_ca_system_score_gemma":0.0009868903,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003891827,"about_ca_topic_score_gemma":0.006582256,"domain_scores_codex":[0.9996669,0.00005787857,0.00001824491,0.00007384561,0.0001393305,0.00004381847],"domain_scores_gemma":[0.9997649,0.00004758713,0.0000301178,0.0000206858,0.0001249972,0.00001174945],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002544892,0.0001123554,0.001557179,0.0004531986,0.0001303446,0.0002494862,0.000171149,0.748541,0.06950168,0.0346707,0.00374773,0.1406107],"study_design_scores_gemma":[0.00001445515,0.0001101903,0.0002715573,0.00001719634,0.00002421186,0.00005267126,0.00001521873,0.9820901,0.01184952,0.001788637,0.00375646,0.000009699648],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03465325,0.000387329,0.9483113,0.0002517093,0.0001169361,0.000156977,0.000107987,0.0009662797,0.01504833],"genre_scores_gemma":[0.8056259,0.0002318261,0.1873388,0.0001835129,0.00003461954,0.0002565093,0.0001502778,0.00005828616,0.006120195],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003891827,"threshold_uncertainty_score":0.008469164,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05797396146599015,"score_gpt":0.2587032871959107,"score_spread":0.2007293257299205,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}